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<ul><li><p>The Eleventh International Conference on Bioinformatics </p><p> Poster Presentations </p><p>#1: Annotation of Long Metagenomic Sequences, Prokaryotic and Eukaryotic </p><p>Presenting Author: Alexandre Lomsadze Email: </p><p>Complete Author List: Alexandre Lomsadze (1,2); Liexiao Ding (5); Wenhan Zhu (2); Mark Borodovsky (1,2,3,4) </p><p>(1) Joint Georgia Tech and Emory Wallace H Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA (2) Center for Bioinformatics and Computational Genomics, Georgia Tech, Atlanta, GA (3) School of Computational Science and Engineering, Georgia Tech, Atlanta, GA (4) Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia (5) H. Milton Stewart School of Industrial &amp; Systems Engineering, Georgia Tech, Atlanta, GA </p><p>Abstract: </p><p>Assembly of metagenomic reads into longer contigs becomes a standard step in metagenomics studies. The increase in the contig length presents an opportunity to improve metagenome annotation. We describe a new version of the gene prediction algorithm, MetaGeneMark-2, that assigns locally optimized parameters to each ORF in a metagenomic contig and improves prediction accuracy in contigs with heterogeneous GC composition. Also, the longer contigs of eukaryotic metagenomics sequences (fungi and protists) provide more data for learning the parameters of a eukaryotic gene finder. The new algorithm selects a model from a set of kingdom specific heuristic models pre-built for a wide range of GC contents. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#2: Improved Prokaryotic Gene Prediction Yields Insights into Transcription and Translation Mechanisms on Whole Genome Scale </p><p>Presenting Author: Karl Gemayel Email: </p><p>Complete Author List: Alexandre Lomsadze* (1,2); Karl Gemayel* (3); Shiyuyun Tang* (5); Mark Borodovsky (1,2,3,4); *joint first authors </p><p>(1) Joint Georgia Tech and Emory Wallace H Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA (2) Center for Bioinformatics and Computational Genomics, Georgia Tech, Atlanta, GA (3) School of Computational Science and Engineering Georgia Tech, Atlanta, GA (4) Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia (5) School of Biological Sciences Georgia Tech, Atlanta, GA </p><p>Abstract: </p><p>GeneMarkS-2, a new ab initio gene finder, aims to improve prediction of species-specific (native) genes, as well as difficult-to-detect genes that differ in composition from the native genes. We introduce an array of pre-computed heuristic models that compete with the iteratively learned native model for the best fit within genomic neighborhoods that deviate in nucleotide composition from the genomic mainstream. Also, in the process of self-training, GeneMarkS-2 identifies distinct sequence patterns controlling transcription and translation. We assessed the accuracy of current state-of-the-art gene prediction tools on test sets of genes validated by proteomics experiments, by COG annotation, as well as by protein N-terminal sequencing. We observed that, on average, GeneMarkS-2 shows a higher precision in all accuracy measures. Screening of ~5,000 representative prokaryotic genomes revealed widespread leaderless transcription, not only in archaeal domain where it was originally discovered, but in bacterial domain as well. Furthermore, the RBS sites of the species with prevalent leadered transcription may frequently exhibit consensus patterns different from the conventional ones of Shine-Dalgarno. GeneMarkS-2 distinguishes leaderless and leadered transcription and reveals the prevalence of one or the other, thus, making classification of prokaryotic genomes into five groups with distinct sequence patterns around gene starts. Some of the observed patterns are apparently related to yet poorly characterized mechanisms of translation initiation. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#3: Predicting Health States from Cystic Fibrosis Lung Microbiome Composition </p><p>Presenting Author: Conan Y. Zhao Email: </p><p>Complete Author List: Conan Y. Zhao (1); Karan A. Kapuria (1); Yiqi Hao (1); John Varga (2); Sam P. Brown (1); Joanna B. Goldberg (2) </p><p>(1) Georgia Institute of Technology, School of Biological Science (2) Emory University, Department of Pediatrics </p><p>Abstract: </p><p>Many studies show that patient health status is directly related to their microbiome composition. However, inferring these relations and developing a predictive model for health status is difficult due to the complex dynamics of microbial communities. Many algorithms infer mechanistic models by fitting high-resolution longitudinal data to generalized Lotka-Volterra equations. However, in a patient health context such data can be difficult to obtain. Other methods avoid the need for temporal resolution by calculating correlational information from 16S pyrosequencing snapshot data. Predictions based on microbe correlations, however, can differ greatly from those based on interaction data. We present a novel analysis pipeline for predicting patient health metrics using 16S compositional data. We apply machine learning techniques to fit general Lotka-Volterra models using simulated microbiome data as well as cystic fibrosis (CF) lung microbiome data and metadata from a cohort of 77 patients. We predict community metrics such as stability and antibiotic susceptibility and compare our results against other commonly used correlational analysis techniques. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#4: Staphopia: an analysis pipeline and Application Programming Interface focused on Staphylococcus aureus. </p><p>Presenting Author: Robert A. Petit III Email: </p><p>Complete Author List: Robert A. Petit III (1); Timothy D. Read (1,2) </p><p>(1) Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, USA (2) Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA </p><p>Abstract: </p><p>Rapid low-cost sequencing of clinically-important bacterial pathogens has generated thousands of publicly available datasets and many hundreds of thousands more will undoubtedly soon be generated. Analyzing these genomes and extracting relevant information for each pathogen and the associated clinical phenotypes requires not only resources and bioinformatic skills but domain knowledge on the nuances of the organism. We have created an analysis pipeline and API focused on Staphylococcus aureus, which is not only a common human commensal but is also of important public health interest, with MRSA (methicillin-resistant S. aureus) a major antibiotic-resistant hospital pathogen. Staphopia can be used both for basic science studies (e.g. patterns of evolution) but also potentially as a platform for rapid clinical diagnostics. Written in Python, Staphopias analysis pipeline consists of submodules running open-source tools managed by a pipeline manager. It accepts raw FASTQ reads as an input, which undergo quality control filtration, error correction and reduction to a maximum of 100x coverage. This data reduction is advantageous for load management when processing thousands of genomes. Using preprocessed reads the pipeline branches off into de novo assembly-based analysis and mapping-based analysis. Modules running species-specific analyses such as antibiotic resistance profiling and multi-locus sequence type (MLST), use the contigs. Genes are annotated from the contigs using PROKKA and the UniProt database. Mapping is used to to call all variants (SNPs and InDels) against a single reference chromosome (S. aureus N315). From the processed reads, 31-mers are counted for each input sample. Depending on the size of the input file, analysis is completed between 20-60 minutes. With Staphopias web application, built using the Django web-framework, analysis results from each genome are stored within a PostgreSQL database with the exception of k-mers, which are stored using Elasticsearch. Users can access these results graphically through a web front end ( or programmatically through a web API. We have also written a R package (staphopia-R) to access the API. The pipeline has also been encapsulated into a Docker image, simplifying installation and running on local machines and in the cloud. More information about Staphopia is available at All code is available on public GitHub repos. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#5: Toxicant Exposomics of a Key Bacterial Gut Commensal Carrier of Antibiotic Resistance </p><p>Presenting Author: Stephen P. LaVoie Email: </p><p>Complete Author List: Stephen P. LaVoie (1); Andrew G. Wiggins (1); Anne O. Summers (1) </p><p>(1) Department of Microbiology, University of Georgia, Athens, GA 30602, USA </p><p>Abstract: </p><p>Multi-antibiotic resistant (MAR) bacteria cost billions in medical care and many thousands of lives annually worldwide. Perennial calls to curtail agricultural antibiotic use and to fund new antibiotic discovery have yet to stem the resistance tsunami. That anything other than antibiotic use could drive the spread of MAR bacteria is heresy. However, it has long been known that exposure to common non-antibiotic toxicants promotes evolution of linked arrays of resistances to antibiotics and other toxic substances. The best studied example of a non-antibiotic toxicant enriching MAR is the toxic heavy metal mercury (Hg) to which most of the US population is exposed frequently via seafood and/or continuously via dental amalgam fillings. Widely found transposases and integrases assemble linked arrays of Hg and antibiotic resistance genes on mobile plasmids. Many animal and human lab studies and recent genomic epidemiology firmly support that widespread exposure to toxic metals co-selects mobile MAR in commensal and pathogenic bacteria. We studied metabolomic, proteomic, and transcriptomic effects of mercurials on the common gut commensal, E. coli, an opportunistic multi-resistant cause of urogenital and systemic infections. Specifically, we (1) quantified disruption of electrolyte, metal, and biothiol homeostases by acute inorganic Hg and organic Hg (RHg) compounds; (2) devised a novel global LC/MS-MS proteomics method to identify &gt;300 proteins vulnerable to stable modification by acute RHg or Hg; and (3) used RNA-seq to discover strikingly different global gene expression after sub-acute exposure to RHg or Hg compounds. A notable finding was up-regulation of chromosomally encoded multi-antibiotic resistance genes in a plasmid-free strain of this gut bacterium, priming it, like other stressors, to withstand subsequent antibiotic exposure. Building on this foundation we are currently doing Hg-exposure transcriptomics of the same E. coli strain carrying the 100 kb conjugative plasmid NR1. This plasmid encodes the Hg resistance (mer) operon, which is genetically linked to the multi-antibiotic resistance IntI1 integron in the 19 kb transposon, Tn21. Our findings will inform pharmaceutical and nutritional interventions to prevent or minimize Hg-provoked co-selection of multi-antibiotic resistant bacteria in the human GI tract. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#6: A diverse microbial consortium drives nitrogen loss in large-scale aquarium sulfur reactors </p><p>Presenting Author: Andrew S. Burns Email: </p><p>Complete Author List: Andrew S. Burns (1); Cory C. Padilla (1); Zoe A. Pratte (1); Kailen Gilde (2); Matthew Regensburger (2); Eric Hall (2); Alistair D.M. Dove (2); Frank J. Stewart (1) </p><p>(1) School of Biological Sciences, Georgia Institute of Technology (2) Georgia Aquarium </p><p>Abstract: </p><p>High levels of nitrate, resulting from the decomposition of organic material, can have a deleterious effect on the health of inhabitants of aquarium systems. While nitrate is readily produced by aerobic nitrifying prokaryotes in aquariums, removal of nitrate is a more difficult process. Typically, nitrate is removed by removing and replacing a portion of the water mass. Water removal and replacement for large aquarium systems such as the Ocean Voyager exhibit at the Georgia aquarium (23,814,000 liters) is not feasible. In these systems, water is shunted through anaerobic vessels containing sulfur and aragonite where denitrifying bacteria facilitate nitrate removal. These bacteria use reduced sulfur speciessuch as sulfide, sulfite, and thiosulfateas electron donors for the reduction of nitrate to dinitrogen gas through the intermediates nitrite, nitric oxide, and nitrous oxide. For the Ocean Voyager exhibit, two independent padseach containing four anaerobic vesselsare used to process 491,400 liters of exhibit water per minute. A multi-faceted omics approach was implemented to determine the microbial, gene, and transcript diversity across multiple potential niche spaces. The diversity of microbial species inhabiting the sulfur pellets, aragonite pellets, and interstitial water for each tower was determined using 16S rRNA amplicon sequencing. Metatranscriptomic and metagenomic sequencing was further applied to two towers from each pad. Distinct communities were formed in each of the independent pads. In one pad, a single operational taxonomic unit (OTU) closely related to Thiobacillus was a major component of the community while Thiobacillus was only present in low levels in the second pad. A major component of both pads was a consortium of numerous Sulfurimonas OTUs with no single Sulfurimonas OTU being dominant in either pad. Differences in the different physical niches were also evident in the denitrification systems. Sulfurimonas OTUs showed highest representation in interstitial water samples compared to the sulfur or aragonite pellets. Metagenomic and metatranscriptomic sequencing also showed diverse gene function both between microbial members of the community and specific niche space. The availability of multiple niches within the physical structure of the denitrification towers as well as the diverse metabolic potential of sulfur oxidation and denitrification created a diverse community facilitating the removal of nitrate. Overall, the use of multiple approaches allowed for the determination of both the microbial and genetic diversity within these complex systems. </p></li><li><p>The Eleventh International Conference on Bioinformatics </p><p>#7: Community assembly in reef fish gill microbiomes </p><p>Presenting Author: Zoe A. Pratte Email: </p><p>Complete Author List: Pratte ZA (1); Hollman RD (1); Besson M (2); Stewart FJ (1) </p><p>(1) School of Biological Sciences, Georgia Institute of Technology (2) Le Centre de Recherches Insulaires et Observatoire de l'Environnement de Polynsie Franaise </p><p>Abstract: </p><p>Teleost fish represent the largest and most diverse of the vertebrate groups and play important roles in food webs, as ecosystem engin...</p></li></ul>